AutoML Leaderboard

Best model name model_type metric_type metric_value train_time single_prediction_time
1_Default_LightGBM LightGBM rmse 170.896 15.61 0.0141
2_Default_Xgboost Xgboost rmse 170.468 5.64 0.0112
3_Default_CatBoost CatBoost rmse 169.979 4.29 0.0199
4_Default_NeuralNetwork Neural Network rmse 170.801 8.31 0.0181
5_Default_RandomForest Random Forest rmse 172.298 7.4 0.0865
10_LightGBM LightGBM rmse 172.388 4.74 0.0089
6_Xgboost Xgboost rmse 171.529 2.83 0.0099
14_CatBoost CatBoost rmse 169.973 3.38 0.0086
18_RandomForest Random Forest rmse 173.056 4.53 0.0856
22_NeuralNetwork Neural Network rmse 171.033 3.66 0.0147
11_LightGBM LightGBM rmse 170.98 4.52 0.0088
7_Xgboost Xgboost rmse 171.658 2.84 0.0106
15_CatBoost CatBoost rmse 170.012 3.17 0.0093
19_RandomForest Random Forest rmse 175.2 6.63 0.0879
23_NeuralNetwork Neural Network rmse 170.741 9.64 0.0147
12_LightGBM LightGBM rmse 170.878 6.03 0.0091
8_Xgboost Xgboost rmse 173.356 5.22 0.0102
16_CatBoost CatBoost rmse 170.001 2.78 0.0085
20_RandomForest Random Forest rmse 170.212 7.36 0.081
24_NeuralNetwork Neural Network rmse 171.3 7.69 0.0148
13_LightGBM LightGBM rmse 171.342 7.36 0.0087
9_Xgboost Xgboost rmse 173.221 4.41 0.011
17_CatBoost CatBoost rmse 170.035 3.01 0.0098
21_RandomForest Random Forest rmse 170.759 7.37 0.0881
25_NeuralNetwork Neural Network rmse 171.258 5.92 0.0146
14_CatBoost_GoldenFeatures CatBoost rmse 169.999 4.99 0.0201
3_Default_CatBoost_GoldenFeatures CatBoost rmse 170.056 3.26 0.0193
16_CatBoost_GoldenFeatures CatBoost rmse 170.086 3.59 0.0165
14_CatBoost_RandomFeature CatBoost rmse 170.043 4.17 0.0111
26_CatBoost CatBoost rmse 169.967 4.77 0.0086
27_CatBoost CatBoost rmse 170.036 3.08 0.0086
28_CatBoost CatBoost rmse 169.973 4.24 0.04
29_CatBoost CatBoost rmse 170.084 3.16 0.0101
30_RandomForest Random Forest rmse 170.698 7.08 0.0783
31_RandomForest Random Forest rmse 170.286 8.68 0.0787
32_Xgboost Xgboost rmse 170.216 2.88 0.0104
33_Xgboost Xgboost rmse 171.038 3.5 0.0103
34_NeuralNetwork Neural Network rmse 170.513 5.88 0.0147
35_RandomForest Random Forest rmse 171.47 4.31 0.0787
36_RandomForest Random Forest rmse 170.389 8.48 0.0821
37_NeuralNetwork Neural Network rmse 170.833 4.91 0.0147
38_NeuralNetwork Neural Network rmse 171.007 6.74 0.0148
39_LightGBM LightGBM rmse 170.904 4.68 0.0087
40_LightGBM LightGBM rmse 170.879 4.88 0.011
41_Xgboost Xgboost rmse 170.908 3.15 0.0106
42_Xgboost Xgboost rmse 172.349 3.6 0.0107
43_CatBoost CatBoost rmse 169.976 4.74 0.0086
44_CatBoost CatBoost rmse 169.947 5.98 0.0085
45_CatBoost CatBoost rmse 169.972 4.1 0.0094
46_CatBoost CatBoost rmse 169.981 4.02 0.0085
47_RandomForest Random Forest rmse 170.328 7.12 0.099
48_Xgboost Xgboost rmse 170.246 3.36 0.0106
49_Xgboost Xgboost rmse 170.262 2.95 0.0099
50_RandomForest Random Forest rmse 170.199 7.66 0.0874
51_Xgboost Xgboost rmse 170.467 4.28 0.0407
52_Xgboost Xgboost rmse 170.445 8.25 0.0102
53_NeuralNetwork Neural Network rmse 170.454 6.28 0.0284
54_NeuralNetwork Neural Network rmse 170.611 6.11 0.0173
55_NeuralNetwork Neural Network rmse 170.515 9.52 0.0171
56_NeuralNetwork Neural Network rmse 170.386 10.3 0.0155
57_LightGBM LightGBM rmse 170.387 4.88 0.0088
58_LightGBM LightGBM rmse 171.3 7.61 0.0154
59_LightGBM LightGBM rmse 170.365 5.21 0.0088
60_LightGBM LightGBM rmse 171.297 7.24 0.0087
the best Ensemble Ensemble rmse 169.734 2.84 0.125

AutoML Performance

AutoML Performance

AutoML Performance Boxplot

AutoML Performance Boxplot

Features Importance (Original Scale)

features importance across models

Scaled Features Importance (MinMax per Model)

scaled features importance across models

Spearman Correlation of Models

models spearman correlation